ABSTRACT
Background: The COVID-19 pandemic has caused societal disruption globally and South America has been hit harder than other lower-income regions. This study modeled effects of 6 weather variables on district-level SARS-CoV-2 reproduction numbers (R t ) in three contiguous countries of Tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors. Methods: Daily time-series data on SARS-CoV-2 infections were sourced from health authorities of the three countries at the smallest available administrative level. R t values were calculated and merged by date and unit ID with variables from a Unified COVID-19 dataset and other publicly available sources for May - December 2020. Generalized additive models were fitted. Findings: Relative humidity and solar radiation were inversely associated with SARS-CoV-2 R t . Days with radiation above 1,000 KJ/m2 saw a 1.3%, and those with humidity above 50%, a 0.9% reduction in R t . Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with least population mobility. Wind speed, temperature, region, aggregate government policy response and population age structure had little impact. The fully adjusted model explained 4.3% of R t variance. Interpretation: Dry atmospheric conditions of low humidity increase, and higher solar radiation decrease district-level SARS-CoV-2 reproduction numbers, effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures. Funding: NASA's Group on Earth Observations Work Programme (16-GEO16-0047).
ABSTRACT
The explosive volcanic eruptions of La Soufrière volcano, St Vincent and the Grenadines, in April 2021 caused the displacement of thousands of people, resulting in heavy disruption of livelihoods and economic activities, destruction of critical infrastructure, and volcanic ash deposits that affected the entire mountainous island of St Vincent and the neighboring island of Barbados. The resulting triple crisis in the region included volcanological risks, the prevailing COVID-19 pandemic, and hydrometeorological risks due to the approaching hurricane season. This article analyzes the scientific and operational activities that The University of the West Indies Seismic Research Centre undertook after effusive activity was detected in December 2020, as well as the actions taken during an official response mission of the United Nations, led by the Joint Environment Unit of the United Nations Environment Programme and the United Nations Office for the Coordination of Humanitarian Affairs in Geneva and upon request for international environmental assistance from the Government of St Vincent. It examines the interplay and collaboration between these 2 organizations and other disaster risk reduction agencies. The article also highlights how the interconnected, systemic nature of risks and disasters emphasizes the ultimate need for regional coordination and collaboration across sectors, including scientific monitoring networks, national, regional, and international emergency preparedness and response agencies, academia, and the private sector. The presented case study for elucidating the ongoing lahar hazard at La Soufrière volcano supports a long-term view for planning and mitigation in this challenging topography. This will help to ensure that the volcanic risks in the Caribbean region are appropriately considered a major component of the multihazard approach undertaken by national authorities and scientists to manage community safety and sustainable economic development through adequate means of disaster risk reduction and emergency preparedness.
ABSTRACT
This study is based on an interdisciplinary collaboration between scientists from natural and social sciences to create scientific knowledge about how Twitter is valuable to understand the social impact of hydrometeorological events. The capacity of citizens’ reaction through Twitter to environmental issues is widely analyzed in the current scientific literature. Previous scientific works, for example, investigated the role of social media in preventing natural disasters. This study gives scientific evidence on the existence of diversity in the intentionality of Twitters’ messages related to hydrometeorological events. The methodological design is formed by four experiments implemented in different moments of a temporal axis. The social impact on social media methodology (SISM) is implemented as social media analytics. From the findings obtained, it can be observed that there are different forms of intentionality in Twitter’s messages related to hydrometeorological events depending on the contextual circumstances and on the characteristics of Twitter’s users’ profiles (including the geolocation when this information is available). This content is relevant for future works addressed to define social media communication strategies that can promote specific reactions in vulnerable groups in front the climate change.
ABSTRACT
The geographical occurrence and diffusion of the current COVID-19 pandemic is partly a function of the awareness, socio-economic dynamics, mobility, and health management practices in place. In Nigeria, the first confirmed case of the COVID-19 pandemic was proclaimed on February 27, 2020, in which an Italian citizen was tested positive for the virus in Lagos. Ossiomo watershed in Edo State, Nigeria, is mainly a rural region with limited healthcare access and abundant rains and surface water flowing in different drainage networks. The highly contagious and pathogenic COVID-19 disease, requires effective management of available water resources for sustainable health development. This is because one of the recommendations for preventing COVID-19 is washing hands with soap using running water. In most rural Africa, including Ossiomo, healthcare facilities are inadequate and no sustainable pipe-borne water except rain harvesting for survival. Using Inverse Distance Weighted (IDW) Geographic Information System (GIS) interpolation technique, the rainfall map produced (derived from a 31-year collated geo-coded hydrometeorological data - rainfall and discharge, covering the Ossiomo watershed) shows that rainfall decreases northward with minimum monthly precipitation of 18.8mm in January and to the south with a mean maximum rainfall of 339.0mm in July. NCDC records on Covid-19 were used to create Choropleth maps that revealed very low confirmed cases and relatively moderate-high deaths, though considered relatively low when compared to global statistics. The Pearson Product Moment Correlation Coefficient (PPMCC) further indicates a strong correlation between rainfall and drainage discharge with r=0.717. With sustainable socio-economic activities and adequate water supply, coupled with effective COVID-19 management practices, the pandemic may not linger in the study area.
ABSTRACT
Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS–RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS–RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries.